Dispensing errors and uncertainty: Perspectives of pharmacists in a tertiary health facility in Lagos, Nigeria
Bibliographic record
Abstract
Introduction: Dispensing errors (DE) and health care uncertainty impact on health outcomes in a variety of ways. The aim of initiating therapy is to enhance patient wellness but human error probabilities sometimes cause harm or even fatalities and litigations.Objective: The aims of this study are to discuss the underlying factors in dispensing errors, health care uncertainty and therapeutic outcomes, and to identify the extent of human- and system-based sources of errors by exploring hospital pharmacists’ attitudes and dispositions to DE and uncertainties; and the implications for patient safety in a tertiary hospital.Methods: The study involved a sample of 44 pharmacists who were administered a survey research inventory designed to assess pharmacists’ attitudes and involvement in DE and uncertainty on a variety of important dimensions.Results: Overall the survey research data showed high rating of five human-based dimensions that would minimize dispensing errors, two human-system based, while three system-based (structural) issues were rated as dimensions that would aggravate DE in uncertain health care scenarios.Conclusions: The practical importance of the results for pharmacy practice and therapeutic outcomes are discussed and some suggestions made on how to minimize DE and uncertainty and the policy implications in the hospital pharmacy setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".